Wavelet-Based Circular Hough Transform and its Application in Embryo Development Analysis

Detecting object shapes from images remains a challenging problem in computer vision,
especially in cases where some a priori knowledge of the shape of the objects
of interest exists (such as circle-like shapes) and/or multiple object shapes overlap.
This problem is important in the field of biology,
particularly in the area of early-embryo development,
where the dynamics is given by a set of cells (nearly-circular shapes)
that overlap and eventually divide. We propose an approach to this problem
that relies mainly on a variation of the circular
Hough Transform where votes are weighted by wavelet kernels,
and a fine-tuning stage based on dynamic programming.
The wavelet-based circular Hough transform can be seen as a geometric-driven pulling
mechanism in a set of convolved images,
thus having important connections with well-stablished
machine learning methods such as convolution networks.